Detecting Anomalous Data Using Auto-Encoders

Jerone T. A. Andrews, Edward J. Morton, and Lewis D. Griffin

Abstract—The most general mode of detecting anomalous data would make no assumptions regarding them other than their atypicality. For such a system, choosing features, to best support the detection, is problematic. The hidden layer representation of auto-encoder artificial neural networks is a potential uncommitted solution to this. We have assessed these features on a range of problems derived from two image datasets, feeding the features into one-class Radial Basis Function (RBF) -Support Vector Machine (SVM) classifiers. Our range of problems vary in diversity of the normal and anomalous classes. Assessed across the range, we find the best performing feature to be a late fusion of hidden layer activations, residual error vectors and the raw input signals. This improves upon the use of auto-encoder residual vector error magnitude, which has previously been proposed for anomaly detection.